G2M-checkpoint related immune barrier structure and signature for prognosis and immunotherapy response in hepatocellular carcinoma: insights from spatial transcriptome and machine learning
This study highlights the importance of spatial heterogeneity and machine learning in refining HCC prognosis and ICIs response prediction. G2MRS and KPNA2 emerge as promising biomarkers for personalized HCC management.
Authors
- Xingte Chen
- Hongxin He (ORCID: https://orcid.org/0009-0007-2193-8101)
- Lingdong Shao (ORCID: https://orcid.org/0000-0002-2843-6100)
- Huipeng Fang
- Jingjing Tang (ORCID: https://orcid.org/0000-0002-4339-4789)
- Qizhen Huang (ORCID: https://orcid.org/0000-0001-6689-9264)
- Junxin Wu (ORCID: https://orcid.org/0000-0003-1047-2338)
- Hong Liang (ORCID: https://orcid.org/0000-0002-0579-8651)
- Shiji Wu
- Yaqi Zhong
Institutions
- Fujian Medical University (CN)
- Mengchao Hepatobiliary Hospital (CN)
- Fujian Provincial Cancer Hospital (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1186/s12967-024-06051-4
- Citations
- 8
- Primary Topic
- Ferroptosis and cancer prognosis
- Type
- article
- Field-Weighted Citation Impact
- 12.15